Biology subjects

Devika Ganesamoorthy

Publications and source records attributed to Devika Ganesamoorthy.

3 recordsLinked to original sources

bioRxiv2016

Scaffolding and Completing Genome Assemblies in Real-time with Nanopore Sequencing

Genome assemblies obtained from short read sequencing technologies are often fragmented into many contigs because of the abundance of repetitive sequences. Long read sequencing technologies allow the generation of reads spanning most repeat sequences, providing the opportunity to complete these genome assemblies. However, substantial amounts of sequence data and computational resources are required to overcome the high per-base error rate inherent to these technologies. Furthermore, most existing methods only assemble the genomes after sequencing has completed which could result in either generation of more sequence data at greater cost than required or a low-quality assembly if insufficient data are generated. Here we present the first computational method which utilises real-time nanopore sequencing to scaffold and complete short-read assemblies while the long read sequence data is being generated. The method reports the progress of completing the assembly in real-time so users can terminate the sequencing once an assembly of sufficient quality and completeness is obtained. We use our method to complete four bacterial genomes and one eukaryotic genome, and show that it is able to construct more complete and more accurate assemblies, and at the same time, requires less sequencing data and computational resources than existing pipelines. We also demonstrate that the method can facilitate real-time analyses of positional information such as identification of bacterial genes encoded in plasmids and pathogenicity islands.

Bioinformatics↗

bioRxiv2016

sCNAphase: using haplotype resolved read depth to genotype somatic copy number alterations from low cellularity aneuploid tumors

Accurate identification of copy number alterations is an essential step in understanding the events driving tumor progression. While a variety of algorithms have been developed to use high-throughput sequencing data to profile copy number changes, no tool is able to reliably characterize ploidy and genotype absolute copy number from tumor samples which contain less than 40% tumor cells. To increase our power to resolve the copy number profile from low-cellularity tumor samples, we developed a novel approach which pre-phases heterozygote germline SNPs in order to replace the commonly used B-allele frequency with a more powerful parental-haplotype frequency. We apply our tool - sCNAphase - to characterize the copy number and loss-of-heterozygosity profiles of four publicly available breast cancer cell-lines. Comparisons to previous spectral karyotyping and microarray studies revealed that sCNAphase reliably identified overall ploidy as well as the individual copy number mutations from each cell-line. Analysis of artificial cell-line mixtures demonstrated the capacity of this method to determine the level of tumor cellularity, consistently identify sCNAs and characterize ploidy in samples with as little as 10% tumor cells. This novel methodology has the potential to bring sCNA profiling to low-cellularity tumors, a form of cancer unable to be accurately studied by current methods.

Bioinformatics↗

bioRxiv2015

Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION sequencing

The recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This opens immense potential to shorten the sample-to-results time and is likely to lead to enormous benefits in rapid diagnosis of bacterial infection and identification of drug resistance. However, there are very few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, multi-locus strain typing, gene presence strain-typing and antibiotic resistance profile identification. Using three culture isolate samples as well as a mixed-species sample, we demonstrate that bacterial species and strain information can be obtained within 30 minutes of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 hours. Multi-locus strain typing required more than 15x coverage to generate confident assignments, whereas gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

Bioinformatics↗